To evaluate Marvolo, we used the recent MalConv CNN-powered malware detector [1]}. For context, MalConv's model spans 5 layers, with an embedding layer that maps bytes to vectors, and then a series of convolutional and recurrent layers. Our experiments consider 2 main datasets: (1) the high-accuracy commercial Ember dataset that includes 1.1M samples (800K after removing ill-formed binaries), and (2) the small-scale (free) Brazilian malware dataset [2]} with 50K samples. Given the realism of Ember observed by researchers and practitioners, we use its test set, which consists of 200K benign and malicious samples, directly to reflect malware detection scenarios in the wild. For training, we consider a subset of the 600K-sample Ember training dataset, as well as the full Brazilian dataset; we train a separate MalConv model for each case. Our subsets consist of 10-30K samples, which is consistent with the dataset sizes that many malware research groups currently work with [3]}. While we would have preferred to experiment with augmenting more datasets, we are constrained since many existing datasets do not contain raw binaries as mentioned in .
